Department of Molecular and Biomolecular Physics, National Institute of R&D of Isotopic and Molecular Technologies , 67-103 Donat, 400293 Cluj-Napoca, Romania.
Faculty of Physics, Babeş-Bolyai University , 1 Kogălniceanu, 400084 Cluj-Napoca, Romania.
Anal Chem. 2018 Feb 20;90(4):2484-2492. doi: 10.1021/acs.analchem.7b03124. Epub 2018 Feb 2.
Nonculture-based tests are gaining popularity and upsurge in the diagnosis of invasive fungal infections (IFI) fostered by their main asset, the reduced analysis time, which enables a more rapid diagnosis. In this project, three different clinical isolates of relevant filamentous fungal species were discriminated by using a rapid (less than 5 min) and sensitive surface-enhanced Raman scattering (SERS)-based detection method, assisted by chemometrics. The holistic evaluation of the SERS spectra was performed by employing appropriate chemometric tools-classical and fuzzy principal component analysis (FPCA) in combination with linear discriminant analysis (LDA) applied to the first relevant principal components. The efficiency of the proposed robust algorithm is illustrated on the data set including three fungal isolates (Aspergillus fumigatus sensu stricto, cryptic A. fumigatus complex species, and Rhizomucor pusillus) that were isolated from patient materials. The accurate and reliable discrimination between species of common fungal pathogen strains suggest that the developed method has the potential as an alternative, spectroscopic-based routine analysis tool in IFI diagnosis.
基于非培养的测试在侵袭性真菌感染 (IFI) 的诊断中越来越受欢迎，这主要得益于其主要优势，即缩短了分析时间，从而能够更快地进行诊断。在本项目中，使用一种快速（不到 5 分钟）且灵敏的基于表面增强拉曼散射（SERS）的检测方法，通过化学计量学辅助，区分了三种不同的相关丝状真菌临床分离株。通过使用适当的化学计量学工具——经典和模糊主成分分析（FPCA）与线性判别分析（LDA）相结合，对第一个相关主成分进行整体评估，对 SERS 光谱进行了全面评估。该稳健算法的效率在包括三个真菌分离株（烟曲霉、隐色烟曲霉复合体种和少根根霉）的数据集上得到了说明，这些分离株是从患者材料中分离出来的。对常见真菌病原体菌株进行的准确可靠的物种区分表明，该方法具有作为 IFI 诊断中替代的、基于光谱的常规分析工具的潜力。
